x-voice-match – OpenClaw Skill

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name: x-voice-match description: Analyze a Twitter/X account's posting style and generate authentic posts that match their voice. Use when the user wants to create X posts that sound like them, analyze their posting patterns, or maintain consistent voice across posts. Works with Bird CLI integration.

X Voice Match

Analyze Twitter/X accounts to extract posting patterns and generate authentic content that matches the account owner's unique voice.

Quick Start

Step 1: Analyze the account

cd /data/workspace/skills/x-voice-match
python3 scripts/analyze_voice.py @username [--tweets 50] [--output profile.json]

Step 2: Generate posts

python3 scripts/generate_post.py --profile profile.json --topic "your topic" [--count 3]

Or use the all-in-one approach:

python3 scripts/generate_post.py --account @username --topic "AI agents taking over" --count 5

What It Analyzes

The skill extracts:

• Length patterns: Tweet character counts, thread usage, one-liner vs paragraph style
• Tone markers: Humor style, sarcasm, self-deprecation, edginess level
• Topics: Crypto, AI, tech, memes, personal life, current events
• Engagement patterns: QT vs original, reaction tweets, conversation starters
• Language patterns: Specific phrases, slang, emoji usage, punctuation style
• Content types: Observations, hot takes, memes, threads, questions, personal stories

Output Format

Voice Profile (JSON)

{
  "account": "@gravyxbt_",
  "analyzed_tweets": 50,
  "patterns": {
    "avg_length": 85,
    "length_distribution": {"short": 0.6, "medium": 0.3, "long": 0.1},
    "uses_threads": false,
    "humor_style": "self-deprecating, ironic",
    "topics": ["crypto", "AI agents", "memes", "current events"],
    "engagement_type": "reactive QT heavy",
    "signature_phrases": ["lmao", "fr", "based"],
    "emoji_usage": "minimal, strategic",
    "punctuation": "lowercase, casual"
  }
}

Generated Posts

Returns 1-N posts with confidence scores and reasoning.

Integration with Existing Tools

Works with Bird CLI (/data/workspace/bird.sh):

# Fetch fresh tweets for analysis
./bird.sh user-tweets @gravyxbt_ -n 50 > recent_tweets.txt
python3 scripts/analyze_voice.py --input recent_tweets.txt

Post Type Templates

See references/post-types.md for common X post frameworks:

• Observations
• Hot takes
• Self-deprecating humor
• Crypto commentary
• Reaction posts
• Questions

Advanced Usage

Update Voice Profile

Re-analyze periodically to capture style evolution:

python3 scripts/analyze_voice.py @username --update profile.json

Generate by Post Type

python3 scripts/generate_post.py --profile profile.json --type "hot-take" --topic "crypto"

Batch Generation

python3 scripts/generate_post.py --profile profile.json --batch topics.txt --output posts.json

Workflow

1. First time: Run full analysis on 30-50 tweets
2. Generate posts: Provide topic, get 3-5 style-matched options
3. User picks: Select best option or iterate with feedback
4. Periodic updates: Re-analyze monthly or after major voice shifts

Tips

• Minimum tweets: 30 tweets for basic analysis, 50+ for accuracy
• Recency matters: Recent tweets reflect current style better than old ones
• Topic relevance: Generated posts work best on topics the account normally covers
• Confidence scores: <70% = may not sound authentic, revise or regenerate